Evidence map›Paper›PMID 37963150›Full record

ArticlePloS one2023

A computational analysis of Telegram's narrative affordances.

Tom Willaert

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Tom WillaertBrussels School of Governance, Vrije Universiteit Brussel, Brussels, Belgium.ORCID 0000-0002-3879-6767

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper offers an empirical investigation of the narrative profiles afforded by public, one-way messaging channels on Telegram. We define these narrative profiles in terms of the contribution of messages to a thread of narrative continuity, and test the double hypothesis that 1) Telegram channels afford diverse narrative profiles, corresponding with distinct vernacular uses of the platform's features, and that 2) networks of Telegram channels sampled from thematically distinct seed channels lean towards distinct profiles. To this end, we analyse the textual contents of 2,724,187 messages from 492 public messaging channels spanning five thematic networks. Our computational method builds up the narrative profiles by scrolling down channels and classifying each message according to its narrative fit with the surrounding messages. We thus find that Telegram channels afford several distinct storytelling profiles, which tend to defy traditional notions of narrative coherence. We furthermore observe correspondences between the thematic orientations of channels and their narrative profiles, with a preference for disparate profiles in channels pertaining to conspiracy theories and far-right counterculture, a preference for coherent profiles in channels pertaining to cryptocurrencies, and mixed types in channels pertaining to disinformation about the war in Ukraine. These empirical observations thus inform our further theorization on how platform features allow users to construct and shape narratives online.

Indexed as

NarrationText MessagingCommunicationDisinformationSoftware

Identifiers

PMID37963150
PMCPMC10645302

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.